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Extracting Cardiac and Respiratory Self-Gating Signals from Magnetic Resonance Imaging Data
KTH, School of Technology and Health (STH).
KTH, School of Technology and Health (STH).
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Extrahering av Self-Gating signaler för hjärt- och respirationsrytm från magnetisk resonanstomografi-data (Swedish)
Abstract [en]

Motion artefacts due to cardiac and respiratory motion present a daily challenge in cardiac Magnetic Resonance Imaging (MRI), and many different motion correction procedures are used in clinical routine imaging. To reduce motion artefacts further, patients are required to hold their breath during parts of the data acquisition, which is physically straining – especially when done repetitively. Self-Gating (SG) is a method that extracts cardiac and respiratory motion information from the MRI data in the form of signals, called SG signals, and uses them to divide the data into the specific cardiac and respiratory phases it was acquired from. This method both avoids motion artefacts and allow for free-breathing acquisition. This project’s goal was to find a method for extracting cardiac and respiratory SG signals from MRI data. The data was acquired with a golden angle radial acquisition method for 3-dimensional (3D) scans. Extraction of the raw signal was tested for both raw k-space data and high temporal resolution image series, where the images were reconstructed using a sliding window reconstruction. Filters were then applied to isolate the cardiac and respiratory information, to create separate cardiac and respiratory SG signals. Thereafter trigger points marking the beginning of the cardiac and respiratory cycles were generated. The trigger points were compared against ECG and respiratory trigger points provided by the MR scanner. The conclusion was that the SG signals based on k-space data was functional on the scans from the evaluated subjects and the most effective choice of the two options, but image based SG signals may prove to be functional after further studies.

Abstract [sv]

Rörelseartefakter på grund av hjärt- och respirationsrörelser är idag vardagliga utmaningar inom magnetresonanstomografi (MR) av hjärtat, och många olika metoder används för att eliminera rörelseartefakterna. Patienterna behöver dessutom hålla andan under delar av dataupptagningen, vilket är fysiskt ansträngande – speciellt när det sker upprepade gånger. Self-Gating (SG) är en metod som extraherar information hjärt- och respirationsrytm från MR-datan i form av signaler, kallade SG signaler, och använder dem för att dela in datan i de specifika hjärt- respektive respirationsfaser som var när datan upptogs. Denna metod både undviker rörelseartefakter och tillåter fri andning under dataupptagningen. Målet med det här projektet var att hitta en metod för att extrahera SG signaler för hjärt- och respirationsrytm från MR-data. Datan samlades in med en golden angle radial-upptagning för 3- dimensionella (3D) scanningar. Extraheringen av den råa signalen testades på både rå k-space data och på bildserier av 3D-bilder med hög tidsupplösning, där bilderna var rekonstruerade med en sliding window rekonstruktion. Därefter applicerades filter för att isolera hjärt- och respirationsinformationen, för att få separata SG signaler med endast hjärt- respektive respirationsrytmer. Till slut genererades triggerpunkter för att markera början av hjärt- respektive respirationscyklerna. Dessa jämfördes med triggerpunkter uppmätta med EKG och andningskudde i magnetkameran. Slutsatsen för projektet var att SG signalerna som baserades på k-space data var funktionell för de scanningar som testades och det mest effektiva alternativet, men SG signalerna som baserades på bilder kan visa sig fungera efter mer studier.

Place, publisher, year, edition, pages
2015. , 19 p.
Series
TRITA-STH, 2015:41
Keyword [en]
MRI, MR, magnetic resonance imaging, SG, self-gating, cardiac, cardiac MRI, sliding window reconstruction, motion artefacts, motion correction, 3D, signal filtering
Keyword [sv]
MRI, MR, magnetisk resonanstomografi, SG, self-gating, hjärta, hjärt-MR, sliding window reconstruction, signalfiltrering, rörelseartefakter, rörelsekorrigering, 3D
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-171570OAI: oai:DiVA.org:kth-171570DiVA: diva2:844229
External cooperation
Karolinska Institutet
Subject / course
Medical Engineering
Educational program
Master of Science in Engineering - Medical Engineering
Supervisors
Examiners
Available from: 2015-09-24 Created: 2015-08-04 Last updated: 2015-09-24Bibliographically approved

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